Automatically detecting outlier values in harvested data

ABSTRACT

In an embodiment, a method comprises determining, in received yield data, one or more passes, each pass including a plurality of observations. For each pass of the one or more passes, one or more discrete derivatives are determined, and based on the one or more discrete derivatives first outlier data is generated. First filtered data is generated by removing the first outlier data from the yield data. Furthermore, for each observation in the yield data, a plurality of nearest neighbor observations is determined, and used to determine a plurality of absolute differences in yield values. Based on the plurality of absolute differences, second outlier data is determined. Second filtered data is generated by removing the second outlier data from the first filtered data. Using a presentation layer of a computer system, a graphical representation of the second filtered data is generated and displayed on the computing system.

BENEFIT CLAIM

This application claims the benefit under 35 U.S.C. § 120 as aContinuation of application Ser. No. 16/288,404, filed Feb. 28, 2019,which is a Continuation of application Ser. No. 15/234,920, filed Aug.11, 2016, U.S. Pat. No. 10,238,028, issued Mar. 26, 2019, the entirecontents of which is hereby incorporated by reference for all purposesas if fully set forth herein. The applicants hereby rescind anydisclaimer of claim scope in the parent applications or the prosecutionhistory thereof and advise the USPTO that the claims in this applicationmay be broader than any claim in the parent applications.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2016 The Climate Corporation.

FIELD OF THE DISCLOSURE

The technical field of the present disclosure includes computer systemsuseful in agriculture and climatology. The disclosure is also in thetechnical field of computer systems that are programmed or configured toautomatically detect outlier data values based on digital yield mapdata, pipelined data processing, and computer-implemented datarecommendations for use in agriculture.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

Yield maps are widely used in agricultural management and consist ofstored digital data representing the yield of crops that have been grownin and harvested from an agricultural field. However, many raw yieldmaps contain errors and inaccuracies. In fact, researchers have reportedthat 10% to 50% of the observations included in yield maps areincorrect. Incorrect observations are referred to as outlier data valuesor just outliers.

One advantage of decontaminating raw yield maps to obtain decontaminatedmaps that are free from outliers is that the decontaminated maps areuseful to crop growers. Decontaminated maps can help the growers tocustomize the agricultural practices in terms of improving seedingschedules, irrigation, application of fertilizers such as nitrogen,and/or harvest practices.

SUMMARY

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system 400 uponwhich an embodiment of the invention may be implemented.

FIG. 5 depicts an example embodiment of a timeline view for data entry.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry.

FIG. 7 depicts an example of automated, computer-implemented, yieldoutlier detection pipeline.

FIG. 8 depicts an example computer-automated preprocessing of yielddata.

FIG. 9 depicts an example method for automatically detecting outliers inyield data.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

1. GENERAL OVERVIEW

2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM

-   -   2.1. STRUCTURAL OVERVIEW    -   2.2. APPLICATION PROGRAM OVERVIEW    -   2.3. DATA INGEST TO THE COMPUTER SYSTEM    -   2.4. PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING    -   2.5. IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW

3. CONTAMINATED RAW YIELD DATA

-   -   3.1 TYPES OF DATA CONTAMINATION        -   3.1.1 START PASS DELAY        -   3.1.2 END PASS DELAY        -   3.1.3 FLOW LAG    -   3.2 INITIAL PREPROCESSING OF RAW YIELD MAPS

4. EXAMPLE OF AUTOMATED YIELD OUTLIER DETECTION PIPELINE

5. AUTOMATIC DETECTION OF CONTAMINATION IN YIELD MAPS

-   -   5.1 DETECTING START PASS DELAY OUTLIERS    -   5.2 DETECTING END PASS DELAY OUTLIERS    -   5.3 DETECTING OTHER TYPES OF OUTLIERS        -   5.3.1 LOCAL DIFFERENCE APPROACH        -   5.3.2 SURFACE AREA APPROACH        -   5.3.3 STATISTICAL SPATIAL OUTLIER DETECTION APPROACH

6. LIBRARY FOR AUTOMATIC DETECTION OF CONTAMINATED DATA

7. BENEFITS OF DECONTAMINATED YIELD MAPS

1. General Overview

In an embodiment, an approach for an automatic detection of outliers inyield data maps is presented. An outlier is a potentially incorrect,yield data observation in a yield map that lies outside of the range ofother yield data observations in the map.

An approach for an automatic detection of outliers in yield data mapsmay be implemented as a computer-based library. The library may includea set of programmable function calls and instructions configured toautomatically detect outliers in the yield maps. The implementation mayprovide a computer-based tool available to agricultural researchers andcrop growers. The results generated by the approach presented herein maybe used to help the growers to manage agricultural fields and determineseeding and planting schedules.

In an embodiment, an approach for an automatic filtering of outliers inyield data includes a two-stage outlier detection approach. In a firststage, potentially erroneous yield observations caused by mechanical andharvesting-related problems are identified and removed from the yielddata. In a second stage, data mining approaches are employed to consideryield data observations within local neighborhoods of observations todetermine additional outliers that can be removed from the yield data.

In an embodiment, a method for an automatic detection of outliers inyield data is performed in a data processing system that comprises oneor more processors and one or more non-transitory data storage mediacoupled to the processors. The data processor system stores sequences ofinstructions which, when executed using the processors, cause receivingover a computer network electronic digital data comprising yield dataand representing yields of crops that have been harvested from anagricultural field. The yield data may be provided as yield data maps.The yield data maps, also referred to as maps, may include yieldobservations collected for one year or multiple years. The observationsmay be organized by harvesting passes.

In the context of crop harvesting, a pass is a harvesting cycle duringwhich crop is harvested by a combine harvester. The crop may beharvested using for example, a one-pass method, or a two-pass method. Ina one-pass method, biomass is harvested and recovered simultaneously. Ina two-pass method, harvesting and recovery of biomass material areperformed in separate passes. Typically, each pass is identified by apass identifier.

In an embodiment, a method comprises, using the instructions programmedin the computer system, to determine, in the yield data, one or morepasses, each pass including a plurality of observations.

In an embodiment, a method for an automatic detection of outliers inyield data comprises determining, for each pass of the one or morepasses, one or more discrete derivatives based on a plurality ofobservations included in a pass.

A discrete derivative computed from yield data observations is a rate ofchange defined over a discrete domain of the yield data observations.The rates converging to zero may indicate that the changes in the yielddata observations are relatively small. However, the rates exceeding acertain threshold value may indicate that the changes in the yield dataobservations are relatively large. The yield data observations havingrelatively large rates might be outliers and perhaps were recorded inerror.

One or more discrete derivatives computed based on a plurality ofobservations included in a pass may be used to determine whether any ofthe observations included in the pass are outliers. The discretederivatives may be used to provide a measure of whether the harvestedmass flow, recorded by the observations in the pass, has reached asteady state. If the harvested mass flow has reached a steady state ofthe flow, then the derivatives computed for those observations mayconverge to zero.

However, if the harvested mass flow, recorded in the correspondingobservations, has not reached a steady state, then the derivativescomputed for those observations may exceed a certain threshold value.This may indicate an unsteady state of the flow. The harvested mass flowmay be in an unsteady state during for example, a start pass or an endpass. A start pass is a harvesting pass during which a grain transporterhas not been completely filled in. An end pass is a harvesting passduring which a grain transporter has not been emptied. Some of theobservations that belong to start passes and end passes may be outliers,and thus may be removed from the yield data.

In an embodiment, based on the one or more discrete derivatives, firstoutlier data that includes the outliers is generated, and the process ofidentifying outliers may be repeated for all other passes.

In an embodiment, first filtered data is generated by removing the firstoutlier data from the yield data. The first filtered data is a result ofcompleting a first stage of the approach for an automatic detection ofoutliers in the yield data.

In an embodiment, an approach for an automatic detection of outliers inyield data maps includes a second stage. In a second stage, for eachobservation in the yield data, a plurality of nearest neighborobservations for an observation is determined. A plurality of nearestneighbor observations for an observation may be determined by applying alocal difference approach to a plurality of yield data observations inthe yield data.

For each observation, a plurality of absolute differences in yieldvalues between the observation and each of the plurality of nearestneighbor observations is determined. This may be determined bysuperimposing the yield data onto a rectangular grid, computing asurface area based on the rectangular grid, and using the surface areato determine the absolute differences for the observations.

In an embodiment, based on a plurality of absolute differences computedfor an observation, an outlier score for the observation is determinedand compared with a certain threshold. If the outlier score for theobservation exceeds the certain threshold, then the observation isincluded in second outlier data. The process of identifying observationsfor which outlier scores exceed the certain threshold may be repeatedfor all other observations in the yield data.

In an embodiment, second filtered data is generated by removing thesecond outlier data from the first filtered data. The second filtereddata is a result of completing both a first stage and a second stage ofthe approach for an automatic detection of outliers in yield data maps.

In an embodiment, second filtered data is used to automatically controla computer control system in terms of one or more of seeding practices,irrigation, nitrogen application, and harvesting.

In an embodiment, using a presentation layer of a computer system, agraphical representation of the yields of crops harvested from anagricultural field using only the second filtered data is generated. Thegraphical representation may be also displayed on a computing device.The graphical representation or the second filtered data may also bestored in a storage device, a cloud storage system, a memory unit, orany other system configured to store data.

2. Example Agricultural Intelligence Computer System

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) pesticide data (for example, pesticide, herbicide, fungicide, othersubstance or mixture of substances intended for use as a plantregulator, defoliant, or desiccant, application date, amount, source,method), (g) irrigation data (for example, application date, amount,source, method), (h) weather data (for example, precipitation, rainfallrate, predicted rainfall, water runoff rate region, temperature, wind,forecast, pressure, visibility, clouds, heat index, dew point, humidity,snow depth, air quality, sunrise, sunset), (i) imagery data (forexample, imagery and light spectrum information from an agriculturalapparatus sensor, camera, computer, smartphone, tablet, unmanned aerialvehicle, planes or satellite), (j) scouting observations (photos,videos, free form notes, voice recordings, voice transcriptions, weatherconditions (temperature, precipitation (current and over time), soilmoisture, crop growth stage, wind velocity, relative humidity, dewpoint, black layer)), and (k) soil, seed, crop phenology, pest anddisease reporting, and predictions sources and databases.

A data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of data thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 may have one or more remote sensors 112fixed thereon, which sensors are communicatively coupled either directlyor indirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, unmanned aerialvehicles, and any other item of physical machinery or hardware,typically mobile machinery, and which may be used in tasks associatedwith agriculture. In some embodiments, a single unit of apparatus 111may comprise a plurality of sensors 112 that are coupled locally in anetwork on the apparatus; controller area network (CAN) is example ofsuch a network that can be installed in combines or harvesters.Application controller 114 is communicatively coupled to agriculturalintelligence computer system 130 via the network(s) 109 and isprogrammed or configured to receive one or more scripts to control anoperating parameter of an agricultural vehicle or implement from theagricultural intelligence computer system 130. For instance, acontroller area network (CAN) bus interface may be used to enablecommunications from the agricultural intelligence computer system 130 tothe agricultural apparatus 111, such as how the CLIMATE FIELDVIEW DRIVE,available from The Climate Corporation, San Francisco, Calif., is used.Sensor data may consist of the same type of information as field data106. In some embodiments, remote sensors 112 may not be fixed to anagricultural apparatus 111 but may be remotely located in the field andmay communicate with network 109.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a graphicalscreen display, such as a color display, that is mounted within anoperator's cab of the apparatus 111. Cab computer 115 may implement someor all of the operations and functions that are described further hereinfor the mobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1. The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises code instructions 180. For example, codeinstructions 180 may include data receiving instructions 182 which areprogrammed for receiving, over network(s) 109, electronic digital datacomprising yield data. Code instructions 180 may also include passidentification instructions 187 which are programmed for identifyingpasses in the yield data; first outliers detection instructions 183which are programmed for detecting first outliers in the yield data;discrete derivative instructions 184 which are programmed fordetermining discrete derivatives for the yield data; spatial outlierdetection instructions 185 which are programmed for detecting spatialoutliers in the yield data; second outlier detection instructions 186which are programmed for detecting second outliers in the yield data;and other detection instructions 188.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, and any other structured collection ofrecords or data that is stored in a computer system. Examples of RDBMS'sinclude, but are not limited to including, ORACLE®, MYSQL, IBM® DB2,MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. However, anydatabase may be used that enables the systems and methods describedherein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U.S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 5 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 5, a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline may includeNitrogen, Planting, Practices, and Soil. To add a nitrogen applicationevent, a user computer may provide input to select the nitrogen tab. Theuser computer may then select a location on the timeline for aparticular field in order to indicate an application of nitrogen on theselected field. In response to receiving a selection of a location onthe timeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 5, the top two timelines have the “Fall applied”program selected, which includes an application of 150 lbs. N/ac (poundsof nitrogen per acre) in early April. The data manager may provide aninterface for editing a program. In an embodiment, when a particularprogram is edited, each field that has selected the particular programis edited. For example, in FIG. 5, if the “Fall applied” program isedited to reduce the application of nitrogen to 130 lbs. N/ac, the toptwo fields may be updated with a reduced application of nitrogen basedon the edited program.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 5, the interface may update to indicatethat the “Fall applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Fall applied” program would not alter the April application ofnitrogen.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 6, a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 6. To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 6 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored output values that can serveas the basis of computer-implemented recommendations, output datadisplays, or machine control, among other things. Persons of skill inthe field find it convenient to express models using mathematicalequations, but that form of expression does not confine the modelsdisclosed herein to abstract concepts; instead, each model herein has apractical application in a computer in the form of stored executableinstructions and data that implement the model using the computer. Themodel data may include a model of past events on the one or more fields,a model of the current status of the one or more fields, and/or a modelof predicted events on the one or more fields. Model and field data maybe stored in data structures in memory, rows in a database table, inflat files or spreadsheets, or other forms of stored digital data.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4. The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), Wi-Fi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114. In response to receiving data indicating thatapplication controller 114 released water onto the one or more fields,field manager computing device 104 may send field data 106 toagricultural intelligence computer system 130 indicating that water wasreleased on the one or more fields. Field data 106 identified in thisdisclosure may be input and communicated using electronic digital datathat is communicated between computing devices using parameterized URLsover HTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2, each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprisesaccount-fields-data ingestion-sharing instructions 202 which areprogrammed to receive, translate, and ingest field data from third partysystems via manual upload or APIs. Data types may include fieldboundaries, yield maps, as-planted maps, soil test results, as-appliedmaps, and/or management zones, among others. Data formats may includeshape files, native data formats of third parties, and/or farmmanagement information system (FMIS) exports, among others. Receivingdata may occur via manual upload, e-mail with attachment, external APIsthat push data to the mobile application, or instructions that call APIsof external systems to pull data into the mobile application. In oneembodiment, mobile computer application 200 comprises a data inbox. Inresponse to receiving a selection of the data inbox, the mobile computerapplication 200 may display a graphical user interface for manuallyuploading data files and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into management zones, such as the field map datalayers created as part of digital map book instructions 206. In oneembodiment, the management zones comprise soil zones along with a panelidentifying each soil zone and a soil name, texture, drainage for eachzone, or other field data. Mobile computer application 200 may alsodisplay tools for editing or creating such, such as graphical tools fordrawing management zones, such as soil zones, over a map of one or morefields. Planting procedures may be applied to all management zones ordifferent planting procedures may be applied to different subsets ofmanagement zones. When a script is created, mobile computer application200 may make the script available for download in a format readable byan application controller, such as an archived or compressed format.Additionally and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use. In one embodiment,nitrogen instructions 210 are programmed to provide tools to informnitrogen decisions by visualizing the availability of nitrogen to crops.This enables growers to maximize yield or return on investment throughoptimized nitrogen application during the season. Example programmedfunctions include displaying images such as SSURGO images to enabledrawing of application zones and/or images generated from subfield soildata, such as data obtained from sensors, at a high spatial resolution(as fine as 10 meters or smaller because of their proximity to thesoil); upload of existing grower-defined zones; providing an applicationgraph and/or a map to enable tuning application(s) of nitrogen acrossmultiple zones; output of scripts to drive machinery; tools for massdata entry and adjustment; and/or maps for data visualization, amongothers. “Mass data entry,” in this context, may mean entering data onceand then applying the same data to multiple fields that have beendefined in the system; example data may include nitrogen applicationdata that is the same for many fields of the same grower, but such massdata entry applies to the entry of any type of field data into themobile computer application 200. For example, nitrogen instructions 210may be programmed to accept definitions of nitrogen planting andpractices programs and to accept user input specifying to apply thoseprograms across multiple fields. “Nitrogen planting programs,” in thiscontext, refers to a stored, named set of data that associates: a name,color code or other identifier, one or more dates of application, typesof material or product for each of the dates and amounts, method ofapplication or incorporation such as injected or knifed in, and/oramounts or rates of application for each of the dates, crop or hybridthat is the subject of the application, among others. “Nitrogenpractices programs,” in this context, refers to a stored, named set ofdata that associates: a practices name; a previous crop; a tillagesystem; a date of primarily tillage; one or more previous tillagesystems that were used; one or more indicators of application type, suchas manure, that were used. Nitrogen instructions 210 also may beprogrammed to generate and cause displaying a nitrogen graph, whichindicates projections of plant use of the specified nitrogen and whethera surplus or shortfall is predicted; in some embodiments, differentcolor indicators may signal a magnitude of surplus or magnitude ofshortfall. In one embodiment, a nitrogen graph comprises a graphicaldisplay in a computer display device comprising a plurality of rows,each row associated with and identifying a field; data specifying whatcrop is planted in the field, the field size, the field location, and agraphic representation of the field perimeter; in each row, a timelineby month with graphic indicators specifying each nitrogen applicationand amount at points correlated to month names; and numeric and/orcolored indicators of surplus or shortfall, in which color indicatesmagnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium) application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, hybrid, population, SSURGO, soil tests, orelevation, among others. Programmed reports and analysis may includeyield variability analysis, benchmarking of yield and other metricsagainst other growers based on anonymized data collected from manygrowers, or data for seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2, in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at machine sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 230 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the agricultural apparatus 111 orsensors 112 in the field and ingest, manage, and provide transfer oflocation-based scouting observations to the system 130 based on thelocation of the agricultural apparatus 111 or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or Wi-Fi-based position or mapping apps that areprogrammed to determine location based upon nearby Wi-Fi hotspots, amongothers.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus. Suchcontrollers may include guidance or motor control apparatus, controlsurface controllers, camera controllers, or controllers programmed toturn on, operate, obtain data from, manage and configure any of theforegoing sensors. Examples are disclosed in U.S. patent applicationSer. No. 14/831,165 and the present disclosure assumes knowledge of thatother patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

In another embodiment, sensors 112 and controllers 114 may compriseweather devices for monitoring weather conditions of fields. Forexample, the apparatus disclosed in International Pat. Application No.PCT/US2016/029609 may be used, and the present disclosure assumesknowledge of those patent disclosures.

2.4 Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, and harvestingrecommendations. The agronomic factors may also be used to estimate oneor more crop related results, such as agronomic yield. The agronomicyield of a crop is an estimate of quantity of the crop that is produced,or in some examples the revenue or profit obtained from the producedcrop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truth datasources that compares predicted results with actual results on a field,such as a comparison of precipitation estimate with a rain gauge orsensor providing weather data at the same or nearby location or anestimate of nitrogen content with a soil sample measurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise and distorting effects within the agronomicdata including measured outliers that would bias received field datavalues. Embodiments of agronomic data preprocessing may include, but arenot limited to, removing data values commonly associated with outlierdata values, specific measured data points that are known tounnecessarily skew other data values, data smoothing techniques used toremove or reduce additive or multiplicative effects from noise, andother filtering or data derivation techniques used to provide cleardistinctions between positive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared using cross validationtechniques including, but not limited to, root mean square error ofleave-one-out cross validation (RMSECV), mean absolute error, and meanpercentage error. For example, RMSECV can cross validate agronomicmodels by comparing predicted agronomic property values created by theagronomic model against historical agronomic property values collectedand analyzed. In an embodiment, the agronomic dataset evaluation logicis used as a feedback loop where agronomic datasets that do not meetconfigured quality thresholds are used during future data subsetselection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5 Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3. Contaminated Raw Yield Data

Yield mapping is a process of gathering geo-referenced crop yield datathat has been collected during harvest. The geo-referenced crop yielddata is usually digitally stored in digital yield maps in unprocessedform, and thus often referred to as raw yield data. Raw yield data canbe obtained automatically at harvest from specialized equipmentinstalled on combines and other harvest apparatus.

Raw yield maps may include yield data that was collected within acertain time period, such as a year or several years. The maps may beprovided for crops such as corn, soybeans, wheat, or others. The mapsmay be provided by crop growers, agricultural agencies, governmentalagencies, and other providers. Since the maps are usually unprocessed,the maps often include incorrect data.

Even if yield maps are preliminarily preprocessed by the providers ofthe maps, the maps may still include errors. This is because someproviders use manual and inaccurate methods for removing the errors fromthe maps. The incorrect data in yield maps is often referred to ascontaminated data.

3.1 Types of Data Contamination

Contaminated data may include data that is considered to be incorrect orpotentially incorrect. Such data may include for example, data that wascaptured by malfunctioning sensors of a combine harvester, or data thatwas incorrectly captured by a misaligned harvesting apparatus. Forexample, contaminated data may include data that has been incorrectlyregistered due to improperly calibrated sensors installed in combines.Contaminated data may also include errors introduced by an unresolvedwidth of the harvester bar of the harvesting combine, a varyingharvester speed with which the combine harvests the crops, narrowfinishes of the harvesting passes, and turns and overlaps that thecombine makes as it harvests the crops. The contaminated yieldobservations are referred to herein as outliers.

Outlier contamination in raw yield maps can be attributed to a number ofirregularities occurring when the crop is harvested. Some of theirregularities include time delays caused by the harvesting dynamics aswell as harvesting conditions.

Measurements of yields of crops may also be contaminated when sensorsand measuring apparatus are incorrectly calibrated. The measurements mayalso be contaminated because of the delay between the moment when thecrop is actually cut and the moment when the grain is actually measuredby a sensor installed in a combine harvester. The delay may be measuredas a flow lag, and may correspond to a time difference between the timewhen the crop is cut and the time when the grain reaches a mass flowsensor mounted on the top of the harvester. The error may result inshifting the measurements in such a way that the measurement indicatesthe location of the current grain mass flow that does not correspond tothe actual location from which the grain flow was collected. Forexample, by the time the flow sensor detects the particular mass flow, aGPS location of the combine harvester may change and the delay measuredoften in seconds may not correspond to the GPS readings.

Data representing yields of crops may also be contaminated due to adelay introduced by a grain transporter of a combine harvester. This isoften referred to as a harvester flow mode delay or a start pass delay.The start pass delay may be measured as a delay between a start of thepass indicated by a GPS sensor and the moment when a grain transporterfills at the start of a harvest pass. There also may be a harvester flowmode delay, also referred to as an end pass delay. The end pass delaymay be measured as a delay between the moment when a GPS sensorindicated an end of the harvest pass and the moment when a graintransporter is emptied at the end of the harvest pass.

Measurements of yields of crops may also be contaminated by abruptchanges in the speed with which a combine harvester traverses a field.The abrupt changes in the speed may result in obtaining unrealisticyield measurements. Depending on how abrupt the changes in the speedare, the measurements may be either too high or too low.

Data representing yields of crops may also be contaminated when they arecollected at the time when a combine harvester makes sharp turns. Also,the measurements may be contaminated at the time when a combineharvester changes a bar segment (swath) lengths, which may happen whenthe harvester makes sharp turns. The measurements may also becontaminated when accurate GPS information is not available or cannot beassociated with the measurements. Lack of accurate GPS information canresult in a yield map that is either shifted over the entire field,which is referred to as a systematic error, or is shifted to someincorrect location, which is referred to as a localized error. Asystematic error may affect the entire dataset of measurement, and maybe identified visually since the resulting yield map will not be alignedwith the actual boundaries of the field. A localized error may affect asmall number of measurements, such as the measurements identified ascollected from the same location in the field.

Data contamination may also be caused by local circumstances surroundingthe harvesting process. The circumstances may include dry, humid ordusty conditions present during the harvest that may unduly affectmeasurements of the grain moisture. Since the calculated yield dependson the grain moisture, erroneous moisture measurements may lead toerroneous yield measurements. For example, dry conditions may cause lowgrain moisture, and thus the measurements collected in the dryconditions may be lower than the actual yields of crop. On the otherhand, humid conditions may cause high grain moisture, and thus themeasurements collected in the humid conditions may be higher than theactual yields.

3.1.1 Start Pass Delay

A start pass delay, also referred to as a harvester fill mode delay, isa time delay during which a grain transporter fills at the start of aharvest pass. The start pass delay may be a few minutes long. A startpass delay starts when a pass starts and ends when a combine harvesterreaches a steady state. In an embodiment, yield data measurements thatwere collected during a start pass delay of the harvester areautomatically detected, and may be flagged as contaminated and thenremoved from yield data as contaminated.

3.1.2 End Pass Delay

An end pass delay, also referred to as a harvester finish mode delay, isa time delay during which a grain transporter is being emptied at theend of a harvest pass. In an embodiment, measurements that werecollected during an end pass delay are automatically detected and may beflagged as contaminated and then removed from yield data ascontaminated.

3.1.3 Flow Lag

A flow lag corresponds to the time from the moment when the crop is cutby a combine harvester to the moment when the crop grain reaches themass flow sensor mounted on the top of the harvester. The flow lag is anerror and results in shifting the crop measurements in such a way thatthe current grain mass flow measurement does not correspond to the GPSlocation recorded by the sensor for the current grain measurement. In anembodiment, measurements collected during a flow lag are automaticallydetected and may be flagged as contaminated and then removed from yielddata as contaminated.

3.2 Initial Preprocessing of Raw Yield Maps

Raw yield maps usually contain a vast amount of data. The maps may beespecially large when yield data measurements provided in the maps arerecorded in short time intervals, such as 0.2 second long intervals. Thetypical maps may also contain a large number of contaminated data.

Yield maps provided by certain data sources may be initiallypreprocessed to remove at least some contaminated data. Thepreprocessing may be, however, rough or inaccurate. Therefore, theinitially preprocessed yield maps usually require additional processingto remove the contaminations.

4. Example of Automated Yield Outlier Detection Pipeline

In an embodiment, an automatic detection of outliers in yield data iscarried out using a series of computer-implemented data processing stepsinformally termed a pipeline. The pipeline may be configured to accessvarious data sources that store yield data maps, and to access variousprogrammable libraries that store code instructions for implementing theapproach. The code instructions 180 may be programmed to perform datafiltering, mechanical error detection, statistical outlier detection,data spatial analysis, and other automatic approaches for outlierdetection to implement the pipeline.

FIG. 7 depicts an example of automated, computer-implemented, yieldoutlier detection pipeline. FIG. 7 represents programmed processingsteps and may represent an algorithm for use in programming the codeinstructions 180 and other instructions previously discussed for FIG. 1.In an embodiment, pipeline environment 700 comprises a pipeline 704which is configured to implement an end-to-end process of detecting,flagging and/or removing contaminated yield data from yield maps.

Removal of contaminated data may include removing observations thatinclude errors caused by a flow lag, an end pass delay, a variable speedwith which crops are harvested, a too slow/fast speed with which cropsare harvested, or a short swath/overlap. For example, the outlierscaused by a flow lag may identified by setting a constant lag length anddiscarding those observations that were harvested in time periodssmaller than the constant lag length. The outliers caused by an end passdelay may be detected as harvested in headlands, and may be distributedto preceding observations. The outliers caused by a harvester drivingtoo slow, too fast, or changing a speed too frequently, may beidentified based on the speed information provided for the respectiveobservations. The observations recorded during short swaths or overlapsmay be recorded as already-harvested, and excluded from the measurementsfor the particular area. An automatic approach for detecting thecontaminated data is described below.

Raw yield data may be provided to pipeline 704 by programming system 130(FIG. 1) to receive yield data as part of the field data 106. Or,pipeline 704 may query one or more databases 702 that store yield datamaps for various fields, various time periods, and so forth. Pipeline704 may also query other storage devices and systems, such as a cloudstorage system, a data server, and the like.

Upon receiving yield data, pipeline 704 may determine whether topreprocess the yield data. If the yield data is to be preprocessed, thenprogram instructions in block 706 are executed to preprocess the yielddata.

Block 706 represents program instructions for preprocessing of yielddata. The yield data preprocessing may include yield data checking andverifications. Examples of various preprocessing tasks are provided inFIG. 8.

FIG. 8 depicts an example computer-automated preprocessing of yielddata. In an embodiment, preprocessing 800 includes identifying one ormore outliers caused by for example, mechanical errors 802, and eitherflagging the outliers in the yield data, or removing the outliers fromthe yield data.

Block 804 represents program instructions for checking pass numbers. Inblock 804, yield data is preprocessed to determine whether pass numbersincluded in the yield data are correct. Examples of various tasksperformed in this step are described in block 806. The tasks may includechecking whether pass numbers are recorded correctly, checking whethereach pass has only one associated number, checking whether each passnumber is associated with only one pass, checking whether any pass ismissing, and so forth.

Block 808 represents program instructions for identifying short passes.In block 808, yield data is preprocessed to determine whether any of thepasses in yield data are short passes. Examples of various tasksperformed in this step are described in block 810. The tasks may includeidentifying short passes using the following approach: a pass may beidentified as a short pass if it has a minimum count of observations of30 when its data logging interval is 1, or if it has a minimum count ofobservations of 15 when its data logging interval is 2. Thus,identifying short passes may include identifying those passes that havethe minimum count of observations of 30 if their data logging intervalis 1, or that have the minimum count of observations of 15 if their datalogging interval is 2.

Block 812 represents program instructions for identifying flow lagdelay. In block 812, yield data is preprocessed to determine whether theyield data includes any flow lag delays. Examples of various tasksperformed in this step are described in block 814. The tasks may includedetermining whether a lag between two observations satisfies thefollowing formula: lags=ceiling (1/(time interval))*2. Other formulasfor determining a lag between observations may also be used.

Block 816 represents program instructions for identifying abrupt speedchanges. In block 816, yield data is preprocessed to determine whetherthe yield data includes any observations with an abrupt speed change, atoo-slow speed, or a too-fast speed. Examples of various tasks performedin this step are described in block 818. The tasks may includedetermining whether a speed change between two consecutive points isgreater than 20%. The tasks may also include determining whether arecorded speed is less than 2 mph, or whether a recorded speed isgreater than 7 mph.

Block 820 represents program instructions for identifying short swaths.In block 820, yield data is preprocessed to determine whether the yielddata includes any observations indicating a short swath or overlap.Examples of various tasks performed in this step are described in block822. The tasks may include determining identifying any observationscorresponding to less than 80% of full recorded harvester bar width.

Preprocessing of yield data maps may include additional types ofpreprocessing not described in FIG. 8. The additional preprocessing mayinclude a preprocessing of the yield data to remove outliers caused byerrors other than mechanical errors.

Outliers identified by preprocessing a yield data map may be eitherflagged as potential outliers, or removed from the yield data map.

Referring again to FIG. 7, in an embodiment, computer-implementedpipeline 704 performs a first stage processing 708 of yield data.

Block 708 represents program instructions for performing a first stageprocessing of yield data. In first stage 708, pipeline 704 appliescomputer-implemented filters to the yield data to for example, identify,flag, and/or remove observations caused by start pass delays, end passdelays, flow lags, and the like.

In first stage 708, computer implemented pipeline 704 may refer to oneor more computer-implemented libraries 712, 714. Libraries 712, 714 maybe configured to store various computer programs and code that implementfirst stage processing 708. Pipeline 704 may, for example, querylibraries 712, 714 to request the programmable instructions forimplementing first stage processing 708. Details of the first stageprocessing are described in FIG. 9 (steps 902-912).

Block 710 represents program instructions for performing a second stageprocessing of yield data. In second stage 710, pipeline 704 applies oneor more computer-implemented filters to the yield data to identify,flag, and/or remove outliers using for example, a nearest neighborsapproach, a surface area approach, and/or a statistical spatial outlierdetection approach.

In second stage 710, computer implemented pipeline 704 may refer tolibraries 712, 714 mentioned above. Libraries 712, 714 may be configuredto store various computer programs and code implementing for example, anearest neighbors approach, a surface area approach, and/or astatistical spatial outlier detection approach. Pipeline 704 may forexample, query libraries 712, 714 to request the programmableinstructions for implementing a surface area approach for determiningoutliers caused by a flow lag. Details of the first stage processing aredescribed in FIG. 9 (steps 914-920).

In an embodiment, upon completing second stage 710 processing of a yielddata map, pipeline 704 generates second filtered data. Second filtereddata may be generated by removing from the yield map that yield dataobservations that have been flagged as outliers in steps 706, 708,and/or 710.

In an embodiment, second filtered data is represented in a graphicalform and transmitted to any type of computer display configured todisplay digital data. For example, pipeline 704 may transmit the secondfiltered data to a computer display 716, and display the second filtereddata in a graphical user interface 718. Graphical user interface 718 maybe programmed with widgets or controls to allow the grower to visualizethe data.

Second filtered data may also be provided to a user as a PDF document, aWord document, a set of images, and the like. Second filtered data maybe provided to a user using various data delivery media. For example, itmay be stored in a cloud storage system, a database server, and others.

5. Automatic Detection of Contamination in Yield Maps

Automatic detection of contamination in raw yield maps is a process inwhich yield maps are analyzed and contaminated yield observations areidentified or flagged, and then potentially removed from the maps. In anembodiment, an automatic detection of contaminated yield observations inraw yield maps provided for an agricultural field includes a two-stageoutlier detection process. In a first stage, the raw yield maps areanalyzed to target potential harvesting dynamics that may be responsiblefor erroneous yield observations. In a second stage, data mininglibraries and approaches are employed to determine local neighborhoodswithin the agricultural field and use their local structure to identifyand flag potential outliers. Both stages are directed to detectingoutliers caused by start pass delays, end pass delays, flow lags, andthe like.

FIG. 9 depicts an example method for automatically detecting outliers inyield data maps. In step 902, raw yield data is received. The raw yielddata may be provided in form of a yield map. A map may include yielddata observations collected during harvest of crops from an agriculturalfield. In an embodiment, yield data may include observations that aregrouped by passes. For example, if a yield map includes two passes ofdata, then the map includes a first set of observations that belong tothe first pass, and includes a second set of observations that belong tothe second pass.

Also in step 902, yield data is analyzed to identify one or more passesfor which observations are provided, and to identify a set ofobservations for each of the passes.

In step 906, for each pass, of the one or more passes, one or morediscrete derivatives are determined based on a plurality of observationsincluded in a pass.

One of the objectives for determining discrete derivatives based onobservations included in a pass is to determine whether the observationsin the pass indicate a steady state of the harvested mass flow. Theharvested mass flow, recorded in the corresponding observations, hasreached a steady state of the flow if the derivatives computed for theobservations converge to zero. However, if the derivatives computed forthe observations not only fail to converge to zero, but they exceed acertain threshold value, then the derivatives indicate that theharvested mass flow has not reached a steady state. This state may bereferred to as an unsteady state. A harvested mass flow may be in anunsteady state during for example, a start pass during which a graintransporter has not been completely filled in.

5.1 Detecting Start Pass Delay Outliers

In an embodiment, a differentiation process for a start pass starts withcomputing discrete derivatives based on observations included in a pass.The derivatives may be computed using for example, the followingequation:

$\begin{matrix}{{\lim\limits_{{\Delta\; t}->0}\frac{{f\left( {x + {\Delta\; t}} \right)} - {f(x)}}{\Delta\; t}} = \frac{y_{n} - y_{n - 1}}{\Delta\; t}} & (1)\end{matrix}$where y is a mass flow measured as a product of a count of bushelsharvested per second and a time function in a time domain, where Δt is alogging interval in a time domain, and where n is an index of theobservation.

Typically, derivatives are calculated using a continuous functiondefined over a continuous domain. However, yield maps provide discrete,not continuous, observations. Therefore, in case of the observationsprovided in the yield maps, the logging interval Δt may not converge tozero, and neither may the Δ.

In an embodiment, a discrete derivative computed from discreteobservations is obtained using an approximation. The approximation isused to determine whether the mass flow has reached a steady state.

In an embodiment, one or more absolute values of discrete derivativesfor discrete observations are computed. Furthermore, a certain thresholdvalue is set to for example, 0.1. The certain threshold indicates thebeginning of a steady state.

Moreover, a certain observation may be set as a start of a steady statein the mass flow. For the observations subsequent to the certainobservation, the absolute values of derivatives computed for thoseobservations are less than 0.1.

Selection of a certain threshold has many implications. If the thresholdis too small, and thus too rigid, then it is very likely that no steadystate may be detected. Setting the value of 0.1 to the certain thresholdappears to be slightly conservative and may cause identifying too manyoutliers. On the other hand, setting the threshold to a value largerthan 0.1 may result in not detecting a significant count of theoutliers.

In an embodiment, a differentiation process for a start pass delaystarts with a curve fitting to the observations included in a pass. Thecurve fitting may also include computing continuous derivatives for thefitted curve. Based on the continuous derivatives, one or more turningpoints for the curve at which corresponding derivatives are close to 0.0are determined.

Using a curve fitting approach has, however, some drawbacks. Forexample, there might a large variation in the observations, and thusfitting a curve to the observations may be difficult. Therefore theapproach based on computing discrete derivatives and calculatingabsolute values of the discrete derivatives may be more accurate than acure fitting approach.

5.2 Detecting End Pass Delay Outliers

In an embodiment, a differentiation process for an end pass delay startswith computing discrete derivatives based on observations included inthe end pass. The process of computing the discrete derivatives for theend pass is essentially a backwards implementation of the process forcomputing the discrete derivatives for a start pass delay. The discretederivatives are computed for the end pass to observe when a mass flowleaves the steady state, and thus harvesting of the crops is beingdiminished.

Discrete derivatives for detecting end pass delay outliers may becomputed using for example, the following equation:

$\begin{matrix}{\lim\limits_{{\Delta\; t}->0}\frac{{f\left( {x + {\Delta\; t}} \right)} - {f(x)}}{\Delta\; t}} & (2)\end{matrix}$which is computed by subtracting y_(n) from y_(n-1) and dividing theresult by Δt; where y is a mass flow measured as a product of a count ofbushels harvested per second and a time function in a time domain, whereΔt is a logging interval in a time domain, and where n is an index ofthe observation.

In an embodiment, one or more absolute values of discrete derivativesfor discrete observations are computed. Furthermore a certain thresholdmay be set. The threshold may be set and adjusted according tocharacteristics of the source that provided the data. For example, ifthe data is received from a first source, then the threshold may be setto for example, 0.1. If the data is received from a second source, thenthe threshold may be set to for example, 2.0. The certain thresholdindicates the end of a steady state. Moreover, a certain observation isset as an end of a steady state in the mass flow. For the observationssubsequent to the certain observation, their absolute values ofderivatives are usually greater than 0.1.

Selection of a certain threshold value for an end pass delay has similarimplications as for a start pass delay. If the threshold is too small,and thus too rigid, then it is very likely that an end of the steadystate is not detected. Setting the value of 0.1 to the certain thresholdappears to be slightly conservative and may cause identifying too manyoutliers. On the other hand, the implications of setting values largerthan 0.1 may lead to not detecting a significant amount of the outliers.

Referring again to FIG. 9, also in step 906, based on the one or morediscrete derivatives, a set of unsteady-state observations isdetermined. As described above, if absolute values of discretederivatives exceed a certain threshold for a particular observation,then the observation may correspond to a mass flow being in an unsteadystate.

A set of unsteady-state observations for a pass may include noobservations. This may occur when the pass is neither a start pass noran end pass. This may also occur when even if the pass is either a startpass or an end pass. This may also occur when either the certainthreshold value was set too high, or the observations did not actuallyinclude any outliers.

A set of unsteady-state observations for a pass may include one or moreobservations. This may occur when the pass is either a start pass or anend pass, and the observations collected for the pass indeed includedoutliers.

Also in step 906, a set of unsteady-state observations is included infirst outlier data. First outlier data is the data that has beendetected as potentially including outliers caused by start pass delaysand/or end pass delays. The first outlier data may be modified later byadding additional outliers identified using the approaches describedbelow.

In step 908, a test is performed to determine whether all passes inyield data have been already analyzed for the purpose of identifyingoutliers caused by start pass delays and/or end pass delays. If not allpasses have been already analyzed, then in step 910, a next pass isidentified, and the step 906 is repeated for the observations from thenext pass. However, if all passes have been already analyzed, then step912 is performed.

In step 912, the first outlier data is removed from the yield data. Theremoval of the first outlier data from the yield data causes removingthe outliers identified by processing start pass delays and/or end passdelays from the yield data. This steps ends a first stage of a processof the automatic detection of contamination in raw yield data.

In step 914, a second stage of a process of the automatic detection ofcontamination in raw yield data starts. In this step, one or moreautomated approaches for detecting outliers are implemented. Theautomated approaches may include the approaches for detecting outlierscaused by flow lags.

5.3 Detecting Other Types of Outliers

In an embodiment, an automated process for detecting outliers isimplemented using any of the following approaches: a local differenceapproach, a surface area approach, or a statistical spatial outlierdetection approach. The approaches may be used to detect outliers causedby for example, flow lags. Each of the approaches is described below.

5.3.1 Local Difference Approach

In an embodiment, an automated process for detecting outliers isimplemented by modifying a local difference approach by providing asolution for removing statistical outliers. In step 914, for eachobservation in a yield data map, a set of nearest neighboringobservations are determined. A set generated for a particular yield dataobservation may include for example, eight near neighboringobservations. The neighborhood property may be determined based onlongitude and latitude parameters associated with an observation.

Also in step 914, for each observation, a set of absolute differences isdetermined. An absolute difference for an observation and its neighbormay be computed by for example, computing an absolute value a distancebetween the observation and the neighbor.

In an embodiment, a distance between an observation and a nearestneighbor has an assigned weight. A weight may be assigned according tothe distance. For example, the smaller the distance between anobservation and its neighbor, the larger the weight.

Let assume that an observation is denoted by x, and eight nearestneighbors of x are denoted by (x₁, x₂, x₃, x₄, x₅, x₆, x₇, x₈). A vectorof absolute differences may be denoted as a=(d₁, d₂, d₃, d₄, d₅, d₆, d₇,d₈)=|x-x₁|, |x-x₂|, |x-x₃|, |x-x₄|, |x-x₅|, |x-x₆|, |x-x₇|, |x-x₈|). Avector of inverse-distances may be denoted as:

$\begin{matrix}\left( {\frac{1}{d_{1}},\frac{1}{d_{2}},\ldots\mspace{14mu},\frac{1}{d_{8}}} \right) & (3)\end{matrix}$

In an embodiment, the inverse-distances may be normalized using theirsum. This may be denoted using the following expression:

$\begin{matrix}{\zeta = {\sum\limits_{i = 1}^{8}\frac{1}{d_{i}}}} & (4)\end{matrix}$

The sum from expression (5) may be used to determine a vector weights. Avector of weights may be determined using the following expression:

$\begin{matrix}{w = \left( {\frac{\frac{1}{d_{1}}}{\left| \zeta \right.},\frac{\frac{1}{d_{2}}}{\zeta},\ldots\mspace{14mu},\frac{\frac{1}{d_{8}}}{\zeta}} \right)} & (5)\end{matrix}$

In an embodiment, for each observation, the distances between theobservation and its respective nearest neighbors are multiplied by therespective weights.

In an embodiment, distances computed as either weighted or not, aresummed up across all observations. Then, for each observation, itsrespective sum of distances is compared with the sum derived across allobservations. The comparison may be used to determine those observationsthat may be flagged as outliers in step 914.

In step 914, observations flagged so far are included in second outlierdata. Second outlier data includes the data that has been detected aspotentially including outliers caused by flow lags. The second outlierdata may be modified later by adding additional outliers identifiedusing the approaches described below.

In step 916, a test is performed to determine whether the localdifference approach has been already performed on all observations in ayield data map. If not all observations have been analyzed, then in step918, a next observation is identified and the step 914 is repeated forthe observation. However, if all observations have been alreadyanalyzed, then step 920 is performed.

In step 920, second outlier data is removed from first filtered data.The removal of the second outlier data from the first filtered datacauses removing the outliers identified by performing a local differenceapproach. This steps ends a second stage of a process of the automaticdetection of contamination in raw yield data.

In an embodiment, step 922 is performed. In step 922, a graphicalrepresentation of second filtered data is generated. The graphicalrepresentation of the second filtered data may be then displayed on agraphical user interface on a computer workstation, a laptop, asmartphone, a computer server, or any of the device equipped with acomputer generated display.

Second filtered data may also be stored in a cloud storage device, adatabase server or any other device configured to store data. Secondfiltered data may be saved before storing in any data format designedfor representing electronic data.

One of the benefits of the local difference approach is that theapproach takes into consideration geospatial location and correlationbetween the observations. Furthermore, the approach takes intoconsideration the closeness in terms of a geographical distance betweenthe observations.

5.3.2 Surface Area Approach

In an embodiment, an automated process for detecting outliers isimplemented using a surface area approach. Referring to FIG. 9, thisapproach may be executed in step 914, in which instead of executing alocal difference approach, the surface area approach is executed.

In an embodiment, a surface area approach includes determining arectangular grid for the yield data, and superimposing the yield dataonto the rectangular grid. For example, a particular yield dataobservation, from the yield data, may be associated with a particularpoint on the rectangular grid.

From the yield data observations superimposed on a rectangular grid, athree-dimensional surface for the whole yield data set may be generated.A three-dimensional surface may be generated by for example, treatingthe yield data observations as control points and defining any type ofspline surface over the control points.

In an embodiment, a surface area is calculated for the three-dimensionalsurface generated from yield data points. A surface area may becalculated using various approaches. According to one approach, athree-dimensional surface generated from yield data points superimposedon grid cells, each cell having a square shape.

Let x_(s) denotes a difference between the maximum longitude value andthe minimum longitude value, and y_(s) denotes a difference between themaximum latitude value and the minimum latitude value of theobservations included in the s grid cell. A lateral size of the unitcell may be denoted using the following expression:

$\begin{matrix}{L = \sqrt{\frac{x_{s} \times y_{s}}{N}}} & (6)\end{matrix}$

In an embodiment, the process is repeated for all cells along thehorizontal direction of the grid and for all cells along the verticaldirection of the grid.

In an embodiment, each grid cell is assigned a mean value of yield datapoints whose geospatial location will fall within the cell coordinates.A cell with no data points is assigned the overall mean yield value.

A surface area may be calculated by triangulating the grid and using theassigned yield value of each grid point as its height. Based on, atleast in part, on the surface area information and the grid information,one or more outliers may be identified.

5.3.3 Statistical Spatial Outlier Detection Approach

In an embodiment, an automated process for detecting outliers isimplemented using a statistical spatial outlier detection approach.Referring to FIG. 9, this approach may be executed in step 914, in whichinstead of executing a local difference approach, the statisticaloutlier detection approach is executed.

In an embodiment, a spatial outlier detection include applying one ormore spatial outlier detectors to first filtered data or to any type ofyield data. The detectors may compute scores for the first filtered dataitems, and the data items with extreme scores are flagged as outliers.The data items with extreme scores may be referred to as second filtereddata items or S-outliers.

In an embodiment, an approach based on a spatial outlier detector ismodified by providing a solution for determining a number of neighborsused for outlier detection. A spatial outlier detector usually computesan aggregate function for each measurement by computing the aggregatefunction of the k nearest neighbors of the measurement. The aggregatefunction may be computed as a mean value of the k nearest neighbors or aweighted mean value of the k nearest neighbors. The aggregate functionmay also be computed as a median value, or any other method that allowscapturing spatial auto-correlation between the measurements within theneighborhood. The spatial auto-correlation between the measurementswithin the neighborhood may be determined in a time-space, in alocation-space, and based on any type of characteristics of themeasurements.

A spatial outlier detector may also compute a weighted aggregatefunction for each measurement by determining respective weigh values andcomputing the weighted aggregate function of the k nearest neighbors ofthe measurement. A weighted aggregate function may be computed as aweighted mean value of the k nearest neighbors, and may be used todetermine second outlier data.

Second outlier data may be determined based on weighted spatialcharacteristics. This approach may include computing a weighted meanvalue. In this approach, a set of neighboring first filtered data may bedetermined for a particular first filtered data item in the firstfiltered data. Then, a respective weight value may be determined foreach item in the first filtered data. A weight value determined for adata item may be reversely proportional to the distance between the dataitem and the particular first filtered data item. The data item valuesand the respective weights are used to compute a weighted aggregatedmean value, and the weighted aggregated mean value is used to determinewhether the particular first filtered data item is to be excluded fromthe first filtered data.

The weights may represent different characteristics and criteria. Forexample, the data items in a group of items that were collected withinthe same time interval as a particular data item may have higher weightvalues than the data items in the group that were collected in othertime intervals. Since the data items that were collected in the sametime interval as the particular data will have associated higher weightvalues than the weights of other data items within the group, theweighted aggregate mean value will be influenced primarily by the dataitems that were collected in the same time interval as the particulardata, not by the other data items. Therefore, this approach gives apreferential treatment to the clusters of data items collectedapproximately within the same time interval, and lesser treatment to theother data items within the group of data items.

Other weights may represent a distance-based proximity between dataitems within a group of data items. For example, the data items in agroup of items that were collected from field locations similar to aparticular field location from which a particular data item was locatedmay have higher weight values than the data items in the group that werecollected from other field locations. Since the data items that werecollected from the field locations similar to the particular fieldlocation, the weighted aggregate mean value will be influenced primarilyby the items that were collected from the field locations similar to theparticular field location, and less by the other data values in thegroup. Therefore, this approach gives a preferential treatment to theclusters of data items collected from the closely neighboring fields,and lesser treatment to the other items within the group of data items.

In an embodiment, yield data is cleaned by removing from the yield datathe outliers detected using the approaches described above. The cleanedyield data may be used to automatically control a computer controlsystem of one or more of seeding, irrigation, nitrogen application, andharvesting apparatuses.

6. Library for Automatic Detection of Contaminated Data

In an embodiment, an approach for an automatic detection of contaminateddata in yield data is encoded in program code, and the program codestored in a computer-based library. The program code may include codeinstructions, program calls and routines that can be executed by acomputer. The program code may be invoked as individual calls, or may beimplemented as a tool executed on computer systems.

The library may also provide instructions for generating and displayinga graphical user interface programmed to provide ways for selectingvarious options and for performing an automatic detection ofcontaminated data in yield maps. The graphical user interface may alsobe programmed to provide tools for displaying the results of each of theapproaches, and to allow a user to set parameters for executing thedetector of contaminated data, and to set parameters for displaying theresults of the detector.

The graphical user interface may also be programmable to compare theresults generated using different approaches for the automatic detectionof the contaminated data, and to generate recommendations to cropgrowers and researchers about various aspects of enhancing the yields ofcrops.

7. Benefits of Decontaminated Yield Maps

Using the techniques described herein, computers can generatedecontaminated yield maps based on digital data representing historicalyields harvested from an agricultural field. In addition to enabling thecomputers to generate the decontaminated yield maps, the techniquesherein can also enable the computers to make the decontaminated mapsavailable to crop growers, and enable the computers to generaterecommendations to help the growers to improve their agriculturalpractices.

The presented techniques can also enable the agricultural intelligencecomputing system to automatically decontaminate yield maps, and processthe decontaminated yield maps to derive guidelines for crop growers withrespect to seeding, irrigation, application of fertilizers such asnitrogen, and/or harvesting.

Moreover, the presented techniques can enable the agriculturalintelligence computing system to save computational resources, such asdata storage, computing power, and computer memory of the system, byimplementing a programmable pipeline configured to automaticallygenerate decontaminated data based on digital data. The programmablepipeline can automatically generate recommendations and alerts forfarmers, insurance companies, and researchers, thereby allowing for amore effective agricultural management in the seeding schedules,operations of agricultural equipment, and application of chemicals tofields, protection of crops and other tangible steps in the managementof agricultural field.

What is claimed is:
 1. A method for providing an improvement inautomating outlier detection in harvested data using agriculturalapplications, the method comprising: using instructions programmed in acomputer system, determining, in yield data, one or more passes, eachpass including a plurality of observations; for each pass of the one ormore passes: determining one or more discrete derivatives based on aplurality of observations included in a pass; based on the one or morediscrete derivatives determining a set of unsteady-state observations ofthe plurality of observations for which a crop mass flow is unsteady;including the set of unsteady-state observations in first outlier data;and repeating the step of determining the set of unsteady-stateobservations for all other passes; generating first filtered data byremoving the first outlier data from the yield data; superimposing theyield data onto a rectangular grid; computing a surface area based onthe rectangular grid; using the surface area to determine a set ofobservations within the rectangular grid that includes outliers andincluding the set of observations in second outlier data; generatingsecond filtered data by removing the second outlier data from the firstfiltered data; using a presentation layer of the computer system,generating and causing displaying on a computing device a graphicalrepresentation of yields of crops harvested from an agricultural fieldusing only the second filtered data.
 2. The method of claim 1, whereinthe one or more passes include at least one start pass or at least oneend pass.
 3. The method of claim 1, wherein a start pass is a harvestingpass during which a grain transporter has not been completely filled in;wherein an end pass is a harvesting pass during which a graintransporter is being emptied.
 4. The method of claim 1, furthercomprising determining the one or more discrete derivatives for theplurality of observations based on corresponding mass flow amountsdetermined for discrete time periods.
 5. The method of claim 1, whereinthe second filtered data is used to automatically control a computercontrol system of one or more of seeding, irrigation, nitrogenapplication, or harvesting practices.
 6. The method of claim 1, furthercomprising determining a plurality of nearest neighbor observations foran observation by applying a local difference approach to a plurality ofyield data observations in the yield data.
 7. The method of claim 1,further comprising superimposing the yield data onto a rectangular grid,computing a surface area based on the rectangular grid, using thesurface area to determine a set of observations within the rectangulargrid that includes outliers, and including the set of observations inthe second outlier data.
 8. The method of claim 1, further comprisingdetermining the second outlier data by: for each observation from theyield data, determining a set of neighboring yield data observationsthat were collected either shortly before or shortly after anobservation was collected; computing an aggregate mean value from theset of neighboring yield data observations; and based on, at least inpart, the aggregate mean value, determining whether to include theobservation in the second outlier data.
 9. The method of claim 1,further comprising determining the second outlier data by: for eachobservation from the yield data, determining a set of neighboring yielddata observations that were collected either a first distance before ora second distance after an observation was collected; determining a setof weights for the set of neighboring yield data observations; computinga weighted aggregate mean value from the set of neighboring yield dataobservations and the set of weights; and based on, at least in part, theweighted aggregate mean value, determining whether to include theobservation in the second outlier data; wherein a particular weightvalue for a particular neighbor observation is inversely proportional toa distance value measured between a location at which the particularneighbor observation was collected and a location at which theparticular neighbor observation was collected.
 10. The method of claim1, further comprising generating one or more computer-implementedlibraries comprising programmed instructions for detecting, in the yielddata, observations contaminated due to errors occurring during aharvesting process.
 11. A data processing system for providing animprovement in automating outlier detection in harvested data usingagricultural applications, the data processing system comprising: one ormore processors; one or more non-transitory data storage media coupledto the one or more processors and storing sequences of instructionswhich, when executed using the one or more processors, cause performing:using instructions programmed in a computer system, determining, inyield data, one or more passes, each pass including a plurality ofobservations; for each pass of the one or more passes: determining oneor more discrete derivatives based on a plurality of observationsincluded in a pass; based on the one or more discrete derivativesdetermining a set of unsteady-state observations of the plurality ofobservations for which a crop mass flow is unsteady; including the setof unsteady-state observations in first outlier data; and repeating thestep of determining the set of unsteady-state observations for all otherpasses; generating first filtered data by removing the first outlierdata from the yield data; superimposing the yield data onto arectangular grid; computing a surface area based on the rectangulargrid; using the surface area to determine a set of observations withinthe rectangular grid that includes outliers and including the set ofobservations in second outlier data; generating second filtered data byremoving the second outlier data from the first filtered data; using apresentation layer of the computer system, generating and causingdisplaying on a computing device a graphical representation of yields ofcrops harvested from an agricultural field using only the secondfiltered data.
 12. The data processing system of claim 11, wherein theone or more passes include at least one start pass or at least one endpass.
 13. The data processing system of claim 11, wherein a start passis a harvesting pass during which a grain transporter has not beencompletely filled in; wherein an end pass is a harvesting pass duringwhich a grain transporter is being emptied.
 14. The data processingsystem of claim 11, storing additional sequences of instructions which,when executed using the one or more processors, cause: determining theone or more discrete derivatives for the plurality of observations basedon corresponding mass flow amounts determined for discrete time periods.15. The data processing system of claim 11, wherein the second filtereddata is used to automatically control a computer control system of oneor more of seeding, irrigation, nitrogen application, or harvestingpractices.
 16. The data processing system of claim 11, storingadditional sequences of instructions which, when executed using the oneor more processors, cause: determining a plurality of nearest neighborobservations for an observation by applying a local difference approachto a plurality of yield data observations in the yield data.
 17. Thedata processing system of claim 11, storing additional sequences ofinstructions which, when executed using the one or more processors,cause: superimposing the yield data onto a rectangular grid, computing asurface area based on the rectangular grid, using the surface area todetermine a set of observations within the rectangular grid thatincludes outliers, and including the set of observations in the secondoutlier data.
 18. The data processing system of claim 11, storingadditional sequences of instructions which, when executed using the oneor more processors, cause: determining the second outlier data by: foreach observation from the yield data, determining a set of neighboringyield data observations that were collected either shortly before orshortly after an observation was collected; computing an aggregate meanvalue from the set of neighboring yield data observations; and based on,at least in part, the aggregate mean value, determining whether toinclude the observation in the second outlier data.
 19. The dataprocessing system of claim 11, storing additional sequences ofinstructions which, when executed using the one or more processors,cause: determining the second outlier data by: for each observation fromthe yield data, determining a set of neighboring yield data observationsthat were collected either a first distance before or a second distanceafter an observation was collected; determining a set of weights for theset of neighboring yield data observations; computing a weightedaggregate mean value from the set of neighboring yield data observationsand the set of weights; and based on, at least in part, the weightedaggregate mean value, determining whether to include the observation inthe second outlier data; wherein a particular weight value for aparticular neighbor observation is inversely proportional to a distancevalue measured between a location at which the particular neighborobservation was collected and a location at which the particularneighbor observation was collected.
 20. The data processing system ofclaim 11, storing additional sequences of instructions which, whenexecuted using the one or more processors, cause: generating one or morecomputer-implemented libraries comprising programmed instructions fordetecting, in the yield data, observations contaminated due to errorsoccurring during a harvesting process.